肝癌一直以來是國人十大死因之一。當肝臟發生病變時，如何早期檢測發現並進行治療是有效降低肝癌死亡率相當重要的一環。已有研究指出肝病患者呼出氨氣濃度大於0.7 ppm，而正常人則小於0.3 ppm。本文利用氮化銦(InN)超薄膜(~10 nm)製備具偵測Sub-ppm等級氨氣濃度之氣體感測器，並應用於慢性肝炎患者非侵入性的肝炎呼氣檢測。慢性肝炎患者的呼氣中除了有氨氣外，尚有丙酮、氧氣、二氧化碳及其餘VOC氣體會對氮化銦感測器造成訊號干擾。因此，降低干擾氣體之訊號對低濃度氨氣感測而言益發重要。本文嘗試利用單一感測器以及雙感測器兩種方式提升InN對氨氣選擇性，降低患者呼氣中干擾氣體的影響。
在單一感測器中以五環素(Pentacene)作為表面改質材料提升InN感測器對氨氣的選擇性，並對表面改質前後氨氣與丙酮的訊號差異進行分析。由測量結果可得知，濃度8 ppm之氨氣與丙酮反應比例由1：0.95抑制為1：0.40，證明Pentacene確實具有抑制丙酮氣體的成效；然而對高濃度比例(5 %)的二氧化碳氣體而言，實驗結果可知Pentacene-InN感測器對高濃度二氧化碳並無明顯抑制效果。
為了在高濃度二氧化碳影響下依然能辨別出低濃度氨氣差異，吾人以雙感測器方式進行研究，雙感測器以白金層氮化銦(Pt-InN)以及裸面氮化銦(Bare-InN)組成，利用白金層對氨氣之催化特性，在模擬人體呼氣濃度範圍下：氧氣(16 %–18 %)、二氧化碳(3 %–5 %)，區別0.2 ppm氨氣(模擬正常人濃度)以及0.8 ppm氨氣(模擬肝炎患者濃度)兩種氨氣濃度，並以支援向量機(Support Vector Machine)作為分類方法將其分為兩類。當取樣時間點訂為待測氣體通入後500秒時，正確分類資料點占資料點總數的98%，顯示此方法在氧氣與二氧化碳的干擾下，對於0.2 ppm氨氣資料點及0.8 ppm氨氣資料點依然具有很好的辨別能力。
 

Liver cancer is one of the top ten causes of death in Taiwan. To efficiently increase the survival rate of liver cancer, the development of the detection technique is strongly desired. Some researches indicated that the ammonia concentration in exhaled breath for patients with liver disease was greater than 0.7 ppm, while healthy people have less than 0.3 ppm. We have developed a gas sensor based on ultrathin indium nitride (InN) to detect ammonia at sub-ppm level for diagnosing liver disease. Nevertheless, the InN gas sensor responds to not only ammonia but also other interfering gases (ex: acetone, carbon dioxide, and oxygen) existing in human breath. The interfering gases severely influence the signal captured from the InN sensor, causing difficulty in distinguishing the concentration of ammonia. To reduce the noise from the interfering gases, two methods using single sensor and sensor arrays are proposed in this study to enhance the selectivity of ammonia.
For single sensor, bare InN films have the current variation ratio of 1:0.95 upon exposure to 8 ppm ammonia and 8 ppm acetone, respectively. When the InN surface was decorated with pentacene on the top, the current variation ratio remarkably reduced to 1:0.4, indicating pentacene can suppress acetone signal. However, pentacene cannot be effectively suppressed signals caused by high concentration of carbon dioxide existed in breath. To differentiate sub-ppm ammonia under the variation of high concentrations of carbon dioxide (3-5 %) and oxygen (16-18 %), the sensor arrays composed of Bare-InN and Pt-InN are proposed. The array can differentiate 0.8 ppm and 0.2 ppm ammonia using Support Vector Machine (SVM) in plane formed by different responses of the two sensors. The number of classified data correctly represents 98% of total data points captured at a response time of 500 s.

誌謝 I
中文摘要 II
Abstract III
內容 IV
圖目錄 VI
表目錄 X
符號表 XI
第一章 序論 1
1.1研究背景 :生醫感測器選擇性之重要性 1
1.2肝臟疾病及呼氣檢測 2
1.3氨氣及丙酮氣體簡介 5
1.4研究動機及目的 8
第二章 文獻回顧 9
2.1氮化銦薄膜特性簡介 9
2.2裸面氮化銦薄膜感測氣體機制 11
2.3增進氨氣感測器選擇性之文獻回顧 14
2.3.1表面改質 14
2.3.2溫度調控 18
2.3.3感測器陣列 19
2.3.4表面改質材料選擇 21
2.4五環素(Pentacene)薄膜特性簡介 24
2.5五環素(Pentacene)/氮化銦(InN) 薄膜感測氣體機制 26
2.6白金(Pt)/氮化銦(InN) 薄膜感測氣體機制 27
2.7支援向量機(Support Vector Machines) 29
第三章 元件設計及製作 32
3.1氮化銦感測器概述 32
3.2磊晶成長氮化銦薄膜概述 35
3.3感測器製造流程 37
3.3.1感測元件製程 37
3.3.2加熱器製程 41
3.3.3感測器組裝及打線 44
3.4量測儀器和實驗流程 46
3.4.1儀器設置 46
3.4.2量測方法 55
第四章 結果與討論 56
4.1氨氣與丙酮選擇性之探討 56
4.2氨氣與二氧化碳選擇性之探討 59
4.3二氧化碳與氧氣造成之感測干擾 62
4.4利用Bare-InN與Pt-InN在干擾氣體之下分辨低濃度氨氣 64
4.5 比較不同的取樣時間點所造成的影響 71
4.6在二氧化碳環境下丙酮對氨氣偵測造成之影響: 74
第五章 結論 76
第六章 未來工作 77
參考資料 78

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